IR views language as an open-ended set of mostly stable signs with which texts can be indexed and retrieved, focusing more on a text's potential relevance than its potential meaning.

fLP views language as a system of unstable signs that can be used to talk about the world in creative new ways.


The system can be accessed at: www.educatedinsolence.com/aristotle Our retrieval goals in IR are often affective in nature: we want to find a way of speaking about a topic that expresses a particular sentiment and carries a certain tone.

affective categories are amongst the most cross-cutting structures in language.


Fishlov would dub these non-poetic similes (NPS).

the query "?P @P" will retrieve corpus-attested elaborations of stereotypes in @P to suggest similes of the form "X is as P as P 1 S", where P 1 ∈ ?P.


Using the same clustering algorithm over this feature set, @ X achieves a clustering accuracy (as measured via cluster purity) of 70.2%, compared to 71.96% for Almuhareb and Poesio.

when @ X is used to harvest a further set of attribute nouns for X, via web queries of the form "the P * of X " (where P ∈ @X), then @ X augmented with this additional set of attributes (like hands for surgeon) produces a larger space of 7,183 features.


The AA problem can be naturally posed as one of single-label multiclass classification, with as many classes as candidate authors.

unlike usual text categorization tasks, where the core problem is modeling the thematic content of documents (Sebastiani, 2002), the goal in AA is modeling authors' writing style (Stamatatos, 2009b).


Acceptable performance in AA has been reported with character n-gram representations.

as with word-based features, character n-grams are unable to incorporate sequential information from documents in their original form (in terms of the positions in which the terms appear across a document).


LOWBOW representations have also been applied to discourse segmentation (AMIDA, 2007) and have been suggested for text summarization (Das and Martins, 2007).

to the best of our knowledge the use of the LOWBOW framework for AA has not been studied elsewhere.


As expected, different kernels yield different results.

the diffusion kernel outperformed most of the results obtained with other kernels; confirming the results obtained by other researchers Lafferty and Lebanon, 2005 Table 3: Authorship attribution accuracy when using bags of local histograms and different kernels for word-based and character-based representations.


For these authors sequential information seems to be particularly helpful.

low recognition performance was obtained for authors BL (B. K. Lim) and JM (J. MacArtney).


Indeed, in the absence of gold-standard data, related studies (see Section 2) have been forced to utilize ad hoc procedures for evaluation.

one contribution of the work presented here is the creation of the first largescale, publicly available 6 dataset for deceptive opinion spam research, containing 400 truthful and 400 gold-standard deceptive reviews.


We observe that automated classifiers outperform human judges for every metric, except truthful recall where JUDGE 2 performs best.

16 this is expected given that untrained humans often focus on unreliable cues to deception (Vrij, 2008).


This paper tackles the task of bilingual sentiment analysis.

to previous work, we (1) assume that some amount of sentimentlabeled data is available for the language pair under study, and (2) investigate methods to simultaneously improve sentiment classification for both languages.


We see that the performance of the proposed approach improves steadily by adding more and more unlabeled data.

even with only 2,000 unlabeled sentence pairs, the proposed approach still produces large performance gains.


For example, (Hu and Liu, 2004;Nishikawa et al., 2010) have explored opinion summarization in review domain, and (Paul et al., 2010) summarizes contrastive viewpoints in opinionated text.

opinion summarization in spontaneous conversation is seldom studied.


This debate frame is often not appropriate for contentious issues for similar reasons as the positive/negative frame.

our method does not assume a fixed debate frame, and rather develops one based on the opponents of the contention at hand.


Several works have used the relation between speakers or authors for classifying their debate stance (Thomas et al., 2006, Agrawal et al., 2003.

these works also assume the same debate frame and use the debate corpus, e.g., floor debates in the House of Representatives, online debate forums.


Thus, building a domain specific NER for tweets is necessary, which requires a lot of annotated tweets or rules.

manually creating them is tedious and prohibitively unaffordable.


In our work, gazetteers prove to be substantially useful, which is consistent with the observation of Ratinov and Roth (2009).

the gazetteers used in our work contain noise, which hurts the performance.


Similarly, to study the effectiveness of the CRF model, it is replaced by its alternations, such as the HMM labeler and a beam search plus a maximum entropy based classifier.

to what is reported by Ratinov and Roth (2009), it turns out that the CRF model gives remarkably better results than its competitors.


In this way, we generate dependency-based features for context words such as seee "see" in (seee, flm, +2) (based on the target word flm in the context of flm to seee).

expanded dependency features may introduce noise, and we therefore introduce expanded dependency weights w d ∈ {0.0, 0.5, 1.0} to ameliorate the effects of noise: a weight of w d = 0.0 means no expansion, while 1.0 means expanded dependencies are indistinguishable from non-expanded (strict match) dependencies.


Twitter has been shown to be useful in a number of applications, including tweets as social sensors of realtime events (Sakaki et al., 2010), the sentiment prediction power of Twitter (Tumasjan et al., 2010), etc.

current explorations are still in an early stage and our understanding of Twitter content still remains limited.


Then she chooses a bag of words one by one based on the chosen topic.

not all words in a tweet are closely related to the topic of that tweet; some are background words commonly used in tweets on different topics.


The topic-specific preference value P t (w) for each word w is its random jumping probability with the constraint that w∈V P t (w) = 1 given topic t. A large R t (•) indicates a word is a good candidate keyword in topic t. We denote this original version of the Topical PageRank as TPR.

the original TPR ignores the topic context when setting the edge weights; the edge weight is set by counting the number of co-occurrences of the two words within a certain window size.


We found λ = 0.1 was the optimal parameter for cTPR and TPR.


This idea of consensus-based extraction is also central to our method.

we incorporate this idea into our model by simultaneously clustering output and labeling documents rather than performing the two tasks in serial fashion.


They find significant improvement using this method on data sets consisting of Dutch first names, family names, and geographical names.

it is unclear whether such an approach would be able to improve the performance of the current state-of-the-art G2P systems.


In the training data, many of the sentences are questions directed to a second person, you.

chinese questions do not invert and the subject remains in the canonical first position, thus the transition from the start of sentence to you is highly weighted.


We propose a language-independent method for the automatic extraction of transliteration pairs from parallel corpora.

to previous work, our method uses no form of supervision, and does not require linguistically informed preprocessing.


These systems used a manually labelled set of data for initial supervised training, which means that they are semi-supervised systems.


If we leave open a cell which contains no edges in the maximum likelihood (ML) parse, we incur the cost of additional processing, but are still able to recover the ML tree.

if we close a chart cell which contains an ML edge, search errors occur.


Finally, the size and structure of the grammar is the single largest contributor to parse efficiency.

to the current paradigm, we plan to investigate new algorithms that jointly optimize accuracy and efficiency during grammar induction, leading to more efficient decoding.


Query optimization for logic programming is NP-complete since query optimization for even simple conjunctive database queries is NP-complete (Chandra and Merlin, 1977).

the fact that variables in queries arising from LCFRS rules correspond to the endpoints of spans in the string to be parsed means that these queries have certain structural properties (Gildea, 2011).


Since |V 2 | ≥ 4r 2 , this implies that at least r columns are fully contained in V 2 .

at least 4r +7−(3r −1) = r +8 columns are fully contained in V 1 .


In the inference rule in figure 1 there are 2(r + 1) variables that can be bound to positions in w 1 , and as many that can be bound to positions in w 2 .

the side conditions imply m ij = m ij + |u ij |, for i ∈ {1, 2} and 0 ≤ j ≤ r, and therefore the number of free variables is only r + 1 for each component.


In the case of context-free grammars, this is usually circumvented by casting the rules in binary form prior to epsilon rule elimination.

this is not possible in our case, since SCFGs do not allow normal forms with a constant bound on the length of the right-hand side of each component.


The same holds for the computation of p prefix Secondly, the computation of p prefix G ([v 1 , v 2 a]) for all possible a ∈ Σ may be impractical.

one may also compute the probability that the next part-of-speech in the target translation is A.


In our case both the parser and the supertagger are feature-based models, so from the perspective of a single parse tree, the change is simple: the tree is simply scored by the weights corresponding to all of its active features.

since the features of the supertagger are all Markov features on adjacent supertags, the change has serious implications for search.


This occurs because DD continues to find higher scoring parses at each iteration, and hence the results change.

for BP, even if the marginals have not converged, the minimum risk solution turns out to be fairly stable across successive iterations.


Online learning algorithms like perceptron or the margin-infused relaxed algorithm (MIRA) (Crammer and Singer, 2003) are frequently used for structured problems where Viterbi inference is available.

we find that such methods are unstable on our problem.


", "starbucks" and "coffee" are more general words given the document clusters compared to "joint" and "ventures" (see Fig.2), because they appear more frequently in document clusters.

ttM has no way of knowing that "starbucks" and "coffee" are common terms given the context.


Because HybHSum uses the human generated summaries as supervision during model development and our systems do not, our performance is quite promising considering the generation is completely unsupervised without seeing any human generated summaries during training.

the R-2 evaluation (as well as R-4) w/ stop-words does not outperform other models.


n}, where i is an index corresponding to a particular entity pair (e j , e k ), x i contains all of the sentences with mentions of this pair, and y i = relVector(e j , e k ).

given this form of supervision, we would like to find the setting for θ with the highest likelihood: this objective would be difficult to optimize exactly, and algorithms for doing so would be unlikely to scale to data sets of the size we consider.


Although they do sampling for inference, the global aggregation variables require reasoning about an exponentially large (in the number of sentences) sample space.

our approach required approximately one minute to train and less than one second to test, on the same data.


Conceptually, this is a rather simple approach as all spans of texts are treated uniformly and are being mapped to one of several relation types of interest.

these approaches to RE require a large amount of manually annotated training data to achieve good performance, making it difficult to expand the set of target relations.


Whether this is correct or not is debatable.

to avoid being penalized when our RE system actually correctly predicts the label of an implicit relation, we take the following approach.


Some of the reasons are due to possibly multiple prepositions in between a mention pair, preposition sense ambiguity, pp-attachment ambiguity, etc.

in general, we observe that inferring the structures allows us to discard a large portion of the mention pairs which have no valid relation between them.


Recent work on bilingual Word Sense Disambiguation (WSD) has shown that a resource deprived language (L 1) can benefit from the annotation work done in a resource rich language (L 2) via parameter projection.

this method assumes the presence of sufficient annotated data in one resource rich language which may not always be possible.


Ideally, this threshold (0.6) should have been selected using a development set.

since our work focuses on resource scarce languages we did not want to incur the additional cost of using a development set.


The benefit of bilingual bootstrapping is clearly felt for small seed sizes.

as the seed size increases the performance of the 3 algorithms, viz., MonoBoot, BiBoot and OnlySeed is more or less the same.


assume the existence of an oracle which provides the senses of each word.

conceptResolver automatically determines the number of senses for each word.


In logic, two negatives always cancel each other out.

in language this is only theoretically the case: she is not unhappy does not mean that she is happy; it means that she is not fully unhappy, but she is not happy either.


The proposed representation encodes P&G has sold coffee, but not to airlines.

it is not said that the buyers are likely to have been other kinds of companies.


CCG is one instantiation (Steedman, 2000), which is used by many semantic parsers, e.g., Zettlemoyer and Collins (2005).

the logical forms there can become quite complex, and in the context of program induction, this would lead to an unwieldy search space.


Our model is arc-factored, so we can sum over all DCS trees in Z L (x) using dynamic programming.

in order to learn, we need to sum over {z ∈ Z L (x) : z w = y}, and unfortunately, the additional constraint z w = y does not factorize.


Supervised part-of-speech (POS) taggers, for example, approach the level of inter-annotator agreement (Shen et al., 2007, 97.3% accuracy for English).

supervised methods rely on labeled training data, which is time-consuming and expensive to generate.


The simplest way to expand this formulation for single-type graphs is to duplicate each predicate node, with one node for each order of the types, and then the ILP is unchanged.

this is inefficient as it results in an ILP with 2n(2n − 1) variables and 2n(2n−1)(2n−2) transitivity constraints.


This phrase table is traditionally generated by going through a pipeline of two steps, first generating word (or minimal phrase) alignments, then extracting a phrase table that is consistent with these alignments.

as DeNero and Klein (2010) note, this two step approach results in word alignments that are not optimal for the final task of generating phrase tables that are used in translation.


Estimating such grammars under a Maximum Likelihood criterion is known to be plagued by strong overfitting leading to degenerate estimates (DeNero et al., 2006).

our learning objective not only avoids overfitting the training data but, most importantly, learns joint stochastic synchronous grammars which directly aim at generalisation towards yet unseen instances.


In the RFE estimate, every rule NT P → α / β receives a probability in proportion with the times that α / β was covered by the NT label.

estimating the parameters under Maximum-Likelihood Estimation (MLE) for the latent translation structure model p(σ) is bound to overfit towards memorising whole sentence-pairs as discussed in (Mylonakis and Sima'an, 2010), with the resulting grammar estimate not being able to generalise past the training data.


On the other hand, estimating the parameters under Maximum-Likelihood Estimation (MLE) for the latent translation structure model p(σ) is bound to overfit towards memorising whole sentence-pairs as discussed in (Mylonakis and Sima'an, 2010), with the resulting grammar estimate not being able to generalise past the training data.

apart from overfitting towards long phrase-pairs, a grammar with millions of structural rules is also liable to overfit towards degenerate latent structures which, while fitting the training data well, have limited applicability to unseen sentences.


Earlier approaches for linguistic syntax-based translation such as (Yamada and Knight, 2001;Galley et al., 2006;Huang et al., 2006;Liu et al., 2006) focus on memorising and reusing parts of the structure of the source and/or target parse trees and constraining decoding by the input parse tree.

to this approach, we choose to employ linguistic annotations in the form of unambiguous synchronous span labels, while discovering ambiguous translation structure taking advantage of them.


Parser quality is usually evaluated by comparing its output to a gold standard whose annotations are linguistically motivated.

there are cases in which there is no linguistic consensus as to what the correct annotation is (Kübler et al., 2009).


Therefore, there is no single parameter which completely controls the edge direction, and hence there is no direct way to perform an edge-flip by parameter modification.

setting extreme values for the parameters controlling the direction of a certain edge type creates a strong preference towards one of the directions, and effectively determines the edge direction.


Its induced parent (w 3 ) is its gold child, and thus undirected evaluation does not consider it an error.

w 3 is assigned w 2 's gold parent, w 1 .


Much like undirected evaluation, NED will mark the attachment of w 2 as correct, since its induced parent is its gold child.

unlike undirected evaluation, w 3 's induced attachment will also be marked as correct, as its induced parent is its gold grandparent.


Thus, NED does not accept all possible annotations of structures of size 3.

using a method which accepts all possible annotations of structures of size 3 seems too permissive.


The binary branching nature of CCG means that it is naturally compatible with bottom-up parsing algorithms such as shift-reduce and CKY (Ades and Steedman, 1982;Steedman, 2000).

the parsing work by Clark and Curran (2007), and also Hockenmaier (2003) and Fowler and Penn (2010), has only considered chart-parsing.


A likely reason is that the recovery of longer-range dependencies requires more processing steps, increasing the chance of the correct structure being thrown off the beam.

the precision did not drop more quickly than C&C, and in fact is consistently higher than C&C across all dependency lengths, which reflects the fact that the long range dependencies our parser managed to recover are comparatively reliable.


The shiftreduce parser is linear-time (in both sentence length and beam size), and can analyse over 10 sentences per second on a 2GHz CPU, with a beam of 16, which compares very well with other constituency parsers.

this is no faster than the chart-based C&C parser, although speed comparisons are difficult because of implementation differences (C&C uses heavily engineered C++ with a focus on efficiency).


Here, the Berkeley parser (solid blue edges) incorrectly attaches from debt to the noun phrase $ 30 billion whereas the correct attachment (dashed gold edges) is to the verb raising.

there are a range of error types, as shown in Figure 2 attachment ambiguity where by yesterday afternoon should attach to had already, (b) is an NP-internal ambiguity where half a should attach to dozen and not to newspapers, and (c) is an adverb attachment ambiguity, where just should modify fine and not the verb 's.


We also find their surface marker / punc-  tuation cues of : and , preceding the noun.

we additionally find other cues, most notably that if the N IN sequence occurs following a capitalized determiner, it tends to indicate a nominal attachment (in the n-gram, the preposition cannot attach leftward to anything else because of the beginning of the sentence).


We would like to use additional data to model the fluent part of spoken language.

the Switchboard corpus is one of the largest widelyavailable disfluency-annotated speech corpora.


These units typically represent the most frequent phoneme sequences in English words.

it isn't clear why these units would produce the best hybrid output.


This weight is the negative log probability of the resulting model after adding the corresponding features and priors.

the lexicon prior poses a problem for this construction since the penalty incurred by a new unit in the segmentation depends on whether that unit is present elsewhere in that segmentation.


Humans listening to speech with natural prosody are able to understand the content with low cognitive load and high accuracy.

most modern ASR systems only use an acous-tic model and a language model.


There are also many studies using prosodic information for various spoken language understanding tasks.

research using prosodic knowledge for speech recognition is still quite limited.


Considering that traditional statistical strategies would lead to recall dropping uniformly to 0, this is a vast improvement.

the symbolic baseline recalls 80% of occurrences in aggregate, indicating that we are not yet making optimal use of the dictionary for cases when a code is missing from the training data.


In a typical classroom assessment (e.g., an exam, assignment or quiz), an instructor or a grader provides students with feedback on their answers to questions related to the subject matter.

in certain scenarios, such as a number of sites worldwide with limited teacher availability, online learning environments, and individual or group study sessions done outside of class, an instructor may not be readily available.


When comparing scores using RMSE, the difference is less consistent, yielding an average improvement of 0.002.

for one measure (tf*idf), the improvement is 0.063 which brings its RMSE score close to the lowest of all BOW metrics.


After filtering for duplicates and removing empty or otherwise unusable emails, the total number of emails is 245K, containing roughly 90 million words.

this total includes emails to non-Enron employees, such as family members and employees of other corporations, emails to multiple people, and emails received from Enron employees without a known corporate role.


This annotation method does not take into account promotions over time, secretaries speaking on behalf of their supervisors, or other causes of relationship irregularities.

this misinformation would, if anything, generally hurt our classifiers.


Our feature selection method picks up on indicators suggested by sociolinguistics, and it also allows for the identification of features that are not obviously characteristic of UpSpeak or Down-Speak.

some easily recognizable features include: other features are less intuitive: I'll, we'll "I'll let you know the final results soon" "Everyone is very excited […] and we're confident we'll be successful" Downspeak that is, this is "Neither does any other group but that is not my problem" "I think this is an excellent letter" We hope to improve our methods for selecting and binning features with information theoretic selection metrics and clustering algorithms.


Preliminary experiments incorporating PeerSpeak n-grams yield slightly better numbers.

early results also suggest that the threeway classification problem is made more tractable with cascaded two-way classifiers; feature selection was more manageable with binary problems.


As we will show below, even the simple filters (i)-(v) are sufficient to learn high-quality association scores; this means that we do not need the complex features of "deterministic" systems.

if such complex features are available, then we can use them to improve performance in our self-trained setting.


As we will see below, using N scores acquired from an unlabeled corpus as the only source of information for CoRe performs surprising well.

the weaknesses of this approach are (i) the failure to cover pairs that do not occur in the unlabeled corpus (negatively affecting recall) and (ii) the generation of pairs that are not plausible candidates for coreference (negatively affecting precision).


While one can certainly make use of a more sophisticated feature set, we leave this for future work as our focus is to scale up inference.

it should be noted that this approach is agnostic to the particular set of features used.


(2003) showed that the relative comparison of two candidate antecedents leads to obtaining better accuracy than the pairwise model.

these approaches do not output absolute probabilities, but relative significance between two candidates, and therefore cannot be directly integrated with the ILP-framework.


To our knowledge, FrameNet has not been exploited for coreference resolution.

the use of related verbs is similar in spirit to Bean and Riloff's (2004) use of patterns for inducing contextual role knowledge, and the use of semantic roles is also discussed in Ponzetto and Strube (2006).


If exactly one of NP j and NP k is tagged as a NE by the Stanford NE recognizer, we create a semi-lexical feature that is identical to the lexical feature described above, except that the NE is replaced with its NE label.

if both NPs are NEs, we check whether they are the same string.


To this end, we introduce a rule extraction and weight training method for LMBOT that is based on the corresponding procedures for STSG and STSSG.

general LMBOT can be too expressive in the sense that they allow translations that do not preserve regularity.


STSG are always contiguous in both the left-and right-hand side, which means that they (completely) cover a single span of input or output words.

sTssG rules can be noncontiguous on both sides, but the extraction procedure of sun et al.


Verbs are keys for reordering especially for Araic-English with VSO translated into SVO.

if the verb and its relevant arguments for reordering are at different levels in the tree, the reordering is difficult to model as more interior nodes combinations will distract the distributions and make the model less focused.


2010combined the one class per word type constraint (Brown et al., 1992) in a HMM with a Dirichlet prior to achieve both forms of sparsity.

this work approximated the derivation of the Gibbs sampler (omitting the interdependence between events when sampling from a collapsed model), resulting in a model which underperformed Brown et al.


Sets of dots and optional diacritic markers are used to create character distinctions in Arabic.

trace amounts of dust or dirt on the original document scan can be easily mistaken for these markers (Darwish and Oard, 2002).


As mentioned in Section 4.1, the trivial baseline of the test set is comparable to the dev set.

the test set is harder to tag than the dev set; this can be seen in the overall lower F-scores.


Unlike Modern Hebrew, Latin does not require extensive morpheme segmentation 2 .

it does have a relatively free word order, and is also highly inflected, with each word having up to nine morphological attributes, listed in Table 2.


The parser has no local factors, but has the same variables as the joint model and the same features from all three families of link factors ( §3).

since it takes as input the morphological attributes predicted by the tagger, the TAG variables are now observed.


We find the dis-  tributions over the frequencies of particular errors follow a Zipfian skew across both S&B datasets, with the Arabic being more pronounced (the most frequent error being made 27 times, with 627 errors being made just once) in comparison with the Hebrew (with the most frequent error being made 19 times, and with 856 isolated errors).

in both the Arabic and Hebrew S&B tasks we find that a tendency to over-segment certain characters off of their correct morphemes and on to other frequently occurring, yet incorrect, particles is actually the cause of many of these isolated errors.


A source of statistics widely used in prior work is the query log (Cucerzan and Brill, 2004;Ahmad and Kondrak, 2005;Li et al., 2006a;Chen et al., 2007;Sun et al., 2010).

while query logs are abundant in the context of Web search, in many other search applications (e.g.


A language model was found to outperform a maximum entropy classifier (Gamon, 2010).

the language model was trained on the Gigaword corpus, 17 • 10 9 words (Linguistic Data Consortium, 2003), a corpus several orders of magnitude larger than the corpus used to train the classifier.


Adapting a model so that it takes into consideration the specific error patterns of the non-native writers was shown to be extremely helpful in the context of discriminative classifiers (Rozovskaya and Roth, 2010c;Rozovskaya and Roth, 2010b).

this method requires generating new training data and training a separate classifier for each source language.


To determine typical mistakes, error statistics are collected on a small set of annotated ESL sentences.

for the model to use these language-specific error statistics, a separate classifier for each source language needs to be trained.


Finally, an important performance distinction between the two adapted models is the loss in recall exhibited by AP-adapted -its curve is shorter because AP-adapted is very conservative and does not propose many corrections.

nBadapted succeeds in improving its precision over nB with almost no recall loss.


Second, many of the systems start with the assumption that there is only one type of error.

eSL students often make several combined mistakes in one sentence.


The latter type of article is actually quite challenging to geolocate based on the text content: though the ship is moored in Boston, most of the page discusses its role in various battles along the eastern seaboard of the USA.

such articles make up only a small fraction of the geotagged articles.


When URL, LEX and BOW are removed from the set, performance does not decrease, or only slightly (lines i4, i5, i6), indicating that these three feature groups are least important.

there is significant evidence for the importance of BASE, GAZ, and MISC: removing them decreases performance by at least 1% (lines i2, i3, i7).


This paper is about cross-domain generalization.

the general idea of using search to provide rich context information to NLP systems is applicable to a broad array of tasks.


The human target, owners, is missed because intimidate was not learned.

if owner is in the selectional preferences of the learned 'human target' role, step (2) correctly extracts it into that role.


Our analysis revealed a serious shortcoming: as the discourse relation transitions in short texts are few in number, we have very little data to base the coherence judgment on.

when faced with even short text excerpts, humans can distinguish coherent texts from incoherent ones, as exemplified in our example texts.


In Text (1), a Comparison (Comp) relation would be recorded between the two sentences, irregardless of whether S 1 or S 2 comes first.

it is clear that the ordering of (S 1 ≺ S 2 ) is more coherent.


For instance, the implicit agent in a passive need not be "trivial" but can correspond to an actual discourse referent.

we consider these heuristics as a first step towards capturing an important discourse function of the passive alternation, namely the deletion of the agent role.


Table 3 shows that the proportion of active realisations for the SEM n * input is very high, and the model does not outperform the majority baseline (which always selects active).

the SEM h model clearly outperforms the majority baseline.


According to the global sentence overlap measures, their quality is not seriously impaired.

the design of the representations has a substantial effect on the prediction of the alternations.


word order and voice, by looking at different types of linguistic features and exploring different ways of labelling the training data.

our SVM-based learning framework is not well-suited to directly assess the correlation between a certain feature (or feature combination) and the occurrence of an alternation.


A speaker might already know the answer to a question they asked -for instance, when a teacher is verifying a student's knowledge.

in most cases asking a question represents a lack of authority, treating the other speakers as a source for that knowledge.


We know that there are interesting correlations between these acts and other factors, such as learning gains (Litman and Forbes-Riley, 2006) and the relevance of a contribution for summarization (Wrede and Shriberg, 2003).

adapting dialogue act tags to the question of how speakers position themselves is not straightforward.


Each of these fields of prior work is highly valuable.

none were designed to specifically describe how people present themselves as a source or recipient of knowledge in a discourse.


The contextual model described in section 4.2 performs better than our baseline constrained model.

the gains found in the contextual model are somewhat orthogonal to the gains from using ILP constraints, as applying those constraints to the contextual model results in further performance gains (and a high r 2 coefficient of 0.947).


A translation can potentially have many valid word orderings.

we can be reasonably certain that the ordering of the reference sentence must be acceptable.


In general, the collocations can be automatically identified based on syntactic information such as dependency trees (Lin, 1998).

these me-thods may suffer from parsing errors.


Phrase-based SMT provides a powerful translation mechanism which learns local reorderings, translation of short idioms, and the insertion and deletion of words sensitive to local context.


Phrase-based SMT models dependencies between words and their translations inside of a phrase well.

dependencies across phrase boundaries are largely ignored due to the strong phrasal inde-German English hat er ein buch gelesen he read a book hat eine pizza gegessen has eaten a pizza er he hat has ein a eine a menge lot of butterkekse butter cookies gegessen eaten buch book zeitung newspaper dann then Table 1: Sample Phrase Table   pendence assumption.


We observe a large amount of reordering in the automatically word aligned training text.

given only the source sentence (and little world knowledge), it is not realistic to try to model the reasons for all of this reordering.


Similar to N-gram based MT, it addresses three drawbacks of traditional phrasal MT by better handling dependencies across phrase boundaries, using source-side gaps, and solving the phrasal segmentation problem.

to Ngram based MT, our model has a generative story which tightly couples translation and reordering.


These grammars may use approaches that somewhat reduce the problem of argument-composition, leading to less significant differences between the auxiliary+verb and argument-composition analyses.

planned extensions that cover modification and sub-ordinate clauses will increase local ambiguities.


So, the emission and transition emanating from y n would be characterized as a PCFG rule y n → x n y n+1 .

hMMs factor rule probabilities into emission and transition probabilities: without making this independence assumption, we can model right linear rules directly: So, when we condition emission probabilities on both the current state y n and the next state y n+1 , we have an exact model.


On the one hand, CCM is evaluated using gold standard POS sequences as input, so it receives a major source of supervision not available to the other models.

the other models use punctuation as an indicator of constituent boundaries, but all punctuation is dropped from the input to CCM.


4), the models have fewer natural cues to identify constituents.

within the degrees of freedom allowed by punctuation constraints as described, the chunking models continue to find relatively good constituents.


Fujii and Ishikawa (2002) developed an unsupervised method to find definition sentences from the Web using 18 sentential templates and a language model constructed from an encyclopedia.

we developed a supervised method to achieve a higher precision.


As the examples indicate, many of the extracted paraphrases are not specific to definition sentences and seem very reusable.

there are few paraphrases involving metaphors or idioms in the outputs due to the nature of definition sentences.


In prior work on evaluating independent contributions in content generation, Voorhees (Voorhees, 1998) studied IR systems and showed that relevance judgments differ significantly between humans but relative rankings show high degrees of stability across annotators.

perhaps the closest work to this paper is (van Halteren and Teufel, 2004) in which 40 Dutch students and 10 NLP researchers were asked to summarize a BBC news report, resulting in 50 different summaries.


Finding agreement between annotated welldefined nuggets is straightforward and can be calculated in terms of Kappa.

when nuggets themselves are to be extracted by annotators, the task becomes less obvious.


In previous sections we gave evidence for the diversity seen in human summaries.

a more important question to answer is whether these summaries all cover important aspects of the story.


Using > to denote precedence of semantic groups, some commonly proposed orderings are: quality > size > shape > color > provenance (Sproat and Shih, 1991), age > color > participle > provenance > noun > denominal (Quirk et al., 1974), and value > dimension > physical property > speed > human propensity > age > color (Dixon, 1977).

correctly classifying modifiers into these groups can be difficult and may be domain dependent or constrained by the context in which the modifier is being used.


For example, two clusters with drastically different parts of speech are unlikely to represent the same role.

the converse is not necessarily true as part of speech similarity does not imply role-semantic similarity.


This means we would assign labels to 74% of instances in the dataset (excluding those discarded during argument identification) and attain a role classification with 79.4% precision (purity).

5 instead of labeling all 165, 662 instances contained in these clusters individually we would only have to assign labels to 2, 869 clusters.


As the above example might suggest, the availability of transductive inference for event extraction relies heavily on the known evidences of an event occurrence in specific condition.

the evidence supporting the inference is normally unclear or absent.


The solution is to mine credible evidences of event occurrences from global information and regard that as priori knowledge to predict unknown event attributes, such as that of cross-document and cross-event inference methods.

by analyzing the sentence-level baseline event extraction, we found that the entities within a sentence, as the most important local information, actually contain sufficient clues for event detection.


Most event extraction systems scan a text and search small context windows using patterns or a classifier.

recent work has begun to ex- plore more global approaches.


The fourth sentence describes incidental damage to civilian homes following clashes between government forces and guerrillas.

there is substantial room for improvement in each of TIER's subcomponents, and many role fillers are still overlooked.


Since the web information is used as a black box (including query expansion and query log analysis) which changes over time, it's more difficult to duplicate research results.

gazetteers with entities ranked by salience or major entities marked are worth encoding as additional features.


These could be used for various forms of indirect or distant learning, where instances in a large corpus of such pairs are taken as (positive) training instances.

such instances are noisy -if a pair of entities participates in more than one relation, the found instance may not be an example of the intended relation -and so some filtering of the instances or resulting patterns may be needed.


The core idea is to keep a term-label pair (T, L) only if the number of terms having the label L in the term T's cluster is above a threshold and if L is not the label of too many clusters (otherwise the pair will be discarded).

we are able to add new (high-quality) labels for a term with our evidence propagation method.


Early work on automatic dialogue act classification modeled discourse structure with hidden Markov models, experimenting with lexical and prosodic features, and applying the dialogue act model as a constraint to aid in automatic speech recognition (Stolcke et al., 2000).

to this sequential modeling approach, which is best suited to offline processing, recent work has explored how lexical, syntactic, and prosodic features perform for online dialogue act tagging (when only partial dialogue sequences are available) within a maximum entropy framework (Sridhar, Bangalore, & Narayanan, 2009).


(2010) describe a similar classlabel-based approach for query interpretation, explicitly modeling the importance of each label for a given entity.

details of their implementation were not publicly available, as of publication of this paper.


Increasing the number of label clusters too high, however, significantly reduces precision: CLC-HDP-LG 1000C-200L obtains only ∼51% accuracy.

comparing to CLC-DPMM 1C-40L and CLC-BASE demonstrates that the addition of label clusters and query clusters both lead to gains in label precision.


This result is interesting as the relative prevalence of natural language queries increases with query length, potentially degrading performance.

we did find a strong positive correlation between precision and the number of labels productions applicable to a query, i.e., production rule fertility is a potential indicator of semantic quality.


The label clusters are important because they capture intra-group correlations between class labels, while the query clusters are important for capturing inter-group correlations.

the algorithm is sensitive to the relative number of clusters in each case: Too many labels/label clusters rel-ative to the number of query clusters make it difficult to learn correlations (O(n 2 ) query clusters are required to capture pairwise interactions).


As in the case of other corpora, it is preferable that the size of a learner corpus be as large as possible where the size can be measured in several ways including the total number of texts, words, sentences, writers, topics, and texts per writer.

it is much more difficult to create a large learner corpus than to create a large native-speaker corpus.


Our research follows the direction of the second strand given that consistency can no longer be guaranteed by constructing another phrase table.

to categorically reuse the translations of matched chunks without any differentiation could generate inferior translations given the fact that the context of these matched chunks in the input sentence could be completely different from the source side of the fuzzy match.


This is reflected by an absolute 1.4 point drop in BLEU score and a 1.8 point increase in TER.

both the oracle BLEU and TER scores represent as much as a 2.5 point improvement over the baseline.


We also conjecture that syntactic variations were not captured by the n-gram like string-based features in Section 5.2, therefore resulting in BLEU loss, which will be investigated in future work.

cN has more potential for generating better translations, with the exception of the German-to-English direction, with scores that are usually 10 points better than simple sentence-wise reranking.


(2009a) is different in that both partial and full hypotheses are re-ranked during the decoding phase directly using consensus between translations from different SMT systems.

their method does not change component systems' search spaces.


Both of these two methods share a common limitation: they only re-rank the combined search space, without the capability to generate new translations.

by reusing hypotheses generated by all component systems in HM decoding, translations beyond any existing search space can be generated.


(2008) propose linear BLEU, an approximation to the BLEU score to efficiently perform MBR decoding when the search space is represented with lattices.

our hypotheses space is the full set of finite-length strings in the target vocabulary and can not be represented in a lattice.


The difference in the forward language model score is only 1.58, which may be offset by differences in other features in the log-linear translation model.

the difference in the backward language model score is 3.52.


Note that these tools' objective is to return a single lemma/stem, e.g., they would return adik for adik-beradiknya, and ajar for berpelajaran.

it was straightforward to modify them to also output the above intermediary wordforms, which the tools were generating internally anyway when looking for the final lemma/stem.


(2006), who use cross-lingual pivoting to generate phrase-level paraphrases with corresponding probabilities.

our paraphrases are derived through morphological analysis; thus, we do not need corpora in additional languages.


An early approach by Deng and Byrne (2005) changed the parameterization of the traditional word-based HMM model, modeling subsequent words from the same state using a bigram model.

this model changes only the parameterization and not the set of possible alignments.


As shown in Figure 2, in order to retain the phrasal link f 1 ∼ e 1 , e 2 after agreement, we need the reverse phrasal link e 1 , e 2 f 1 in the backward direction.

this is not possible in a word-based HMM where each observation must be generated by a single state.


The second conclusion is that the co-occurrence model outperforms the context-vector similarity.

both these approaches still perform poorly.


In some other cases, we obtained high precision but poor recall with one feature only, which is not a usefully result as well since most of the correct translations are still labeled as "Non-Translation".

when using both features, the precision is strongly improved up to 98% (English-Spanish or French-Spanish) with a high recall of about 90% for class T. We also achieved about 86%/75% precision/recall in the case of Chinese-English, even though they are very distant languages.


This situation makes the availability of multilingual lexical knowledge a necessary condition to bridge the language gap.

with the only exceptions represented by WordNet and Wikipedia, most of the aforementioned resources are available only for English.


As regards Wikipedia, the crosslingual links between pages in different languages offer a possibility to extract lexical knowledge useful for CLTE.

due to their relatively small number (especially for some languages), bilingual lexicons extracted from Wikipedia are still inadequate to provide acceptable coverage.


(2009) for a complete description of the algorithm.

we note two aspects of our implementation which are important for natural language processing applications.


There, disambiguation is performed using an SVM kernel that compares the lexical context around the ambiguous named entity to the content of the candidate disambiguation's Wikipedia page.

since each ambiguous mention required a separate SVM model, the experiment was on a very limited scale.


Wikipedia's hyperlinks offer a wealth of disambiguated mentions that can be leveraged to train a D2W system.

when compared with mentions from general text, Wikipedia mentions are disproportionately likely to have corresponding Wikipedia pages.


For most Germanic languages like Danish, German, or Swedish, the list of possible linking morphemes is rather small and can be provided manually.

in general, these lists can become very large, and language experts who could provide such lists might not be at our disposal.


While the compounding process for Germanic languages is rather simple and requires only a few linking morphemes, compounds used in Uralic languages have a richer morphology.

to the Germanic and Uralic languages, we did not observe improvements for Greek.


Research in Chinese word segmentation has progressed tremendously in recent years, with state of the art performing at around 97% in precision and recall (Xue, 2003;Gao et al., 2005;Zhang and Clark, 2007;Li and Sun, 2009).

virtually all these systems focus exclusively on recognizing the word boundaries, giving no consideration to the internal structures of many words.


Generation of flat words seen in training is trivial and deterministic since every phrase and word structure rules are lexicalized.

the generation of unknown flat words is a different story.


This is particularly true in the evaluation of Information Retrieval systems where, in fact, the absence of results sometimes is the worse output.

there are scenarios where we should consider the possibility of not responding, because this behavior has more value than responding incorrectly.


According to Column (i), a higher absolute difference is required for concluding that a system is better than another using UF.

the relative difference is similar to the one required by c@1.


Note that the model identifies an incorrect frame REASON for the target discrepancy.N, in turn identifying the wrong semantic role Action for the underlined argument.

the FullGraph model exactly identifies the right semantic frame, SIMILARITY, as well as the correct role, Entities.


Hierarchical Pitman-Yor processes (or their special case, hierarchical Dirichlet processes) have previously been used in NLP, for example, in the context of syntactic parsing (Liang et al., 2007;Johnson et al., 2007).

in all these cases the effective size of the state space (i.e., the number of sub-symbols in the infinite PCFG (Liang et al., 2007), or the number of adapted productions in the adaptor grammar (Johnson et al., 2007)) was not very large.


while a proposition such as "PERSON does THING", has excellent generality, it possesses no discriminating power.


A straightforward solution for important aspect identification is to select the aspects that are frequently commented in consumer reviews as the important ones.

consumers' opinions on the frequent aspects may not influence their overall opinions on the product, and thus not influence consumers' purchase decisions.


Inspection of the corpus shows that approximately 80% of citations indicate agreement, meaning that for the present task the impact of discarding this information may not be large.

the primary utility in collective approaches lies in their ability to fill in gaps in information not picked up by content-only classification.


What we have just described is already partially addressed by the KN model -γ(v) will be relatively large for a productive history like v = in the.

it looks like the KN discounts are not large enough for productive histories, at least not in a combined history-length/class model.


The two methods make use of the information from only one language side.

it is not very reliable to use only the information in one language, because the machine translation quality is far from satisfactory, and thus the translated Chinese sentences usually contain some errors and noises.


With the availability of large-scale annotated corpora such as Penn Treebank (Marcus et al., 1993), it is easy to train a high-performance dependency parser using supervised learning methods.

current state-of-the-art statistical dependency parsers (McDonald et al., 2005;McDonald and Pereira, 2006;Hall et al., 2006) tend to have lower accuracies for longer dependencies (McDonald and Nivre, 2007).


Consider the examples we have shown in section 3.2.2, for sentence (1), the dependency graph path feature ball → with → bat should have a lower weight since ball rarely is modified by bat, but is often seen through them (e.g., a higher weight should be associated with hit → with → bat).

for sentence (2), our N-gram features will tell us that the prepositional phrase is much more likely to attach to the noun since the dependency graph path feature ball → with → stripe should have a high weight due to the high strength of selectional preference between ball and stripe.


(2) Veronis (2005) pointed out that there had been a debate about reliability of Google hits due to the inconsistencies of page hits estimates.


(2010) examine subdomain variation in biomedicine corpora and propose awareness of NLP tools to such variation.

they did not yet evaluate the effect on a practical task, thus our study is somewhat complementary to theirs.


For parsing, these might be words, characters, n-grams (of words or characters), Part-of-Speech (PoS) tags, bilexical dependencies, syntactic rules, etc.

to obtain more abstract types such as PoS tags or dependency relations, one would first need to gather respective labels.


Moreover, the line of selection by topic marker (IN fields) stops early -we believe the reason for this is that the IN fields are too fine-grained, which limits the number of articles that are considered relevant for a given test article.

manually aggregating articles on similar topics did not improve topic-based selection either.


So far, we tested a simple combination of the two by selecting half of the articles by a measure based on words and the other half by a measure based on topic models (by testing different metrics).

this simple combination technique did not improve results yet -topic model alone still performed best.


As Clark and Curran (2004) show, most sentences can be parsed with a very small number of supertags per word.

the technique is inherently approximate: it will return a lower probability parse under the parsing model if a higher probability parse can only be constructed from a supertag sequence returned by a subsequent iteration.


The main reason for this is because a single word in the outside context is in many cases the full stop at the end of the sentence, which is very predictable.

for longer spans the flexibility of CCG to analyze spans in many different ways means that the outside estimate for a nonterminal can be based on many high probability outside derivations which do not bound the true probability very well.


Although this decreases the time required to obtain the highest accuracy, it is still a substantial tradeoff in speed compared with AST.

the AST tradeoff improves significantly: by combining AST with A* we observe a decrease in running time of 15% for the A* NULL parser of the HWDep model over CKY.


Our analysis confirms tremendous speedups, and shows that for weak models, it can even result in improved accuracy.

for better models, the efficiency gains of adaptive supertagging come at the cost of accuracy.


It is clear from our results that the gains from A* do not come as easily for CCG as for CFG, and that agenda-based algorithms like A* must make very large reductions in the number of edges processed to result in realtime savings, due to the added expense of keeping a priority queue.

we have shown that A* can yield real improvements even over the highly optimized technique of adaptive supertagging: in this pruned search space, a 44% reduction in the number of edges pushed results in a 15% speedup in CPU time.


A sound plural example is the word pair / Hafiyd+a /Hafiyd+At 'granddaughter/granddaughters'.

the plural of the inflectionally and morphemically feminine singular word madras+a 'school' is the word madAris+φ 'schools', which is feminine and plural inflectionally, but has a masculine singular suffix.


(2009) and Jiang and Liu (2010) can learn from partially projected trees.

the discriminative training in (Ganchev et al., 2009) doesn't allow for richer syntactic context and it doesn't learn from all the relations in the partial dependency parse.


Local event extraction models (Björne et al., 2009) do not have this limitation, because their local decisions are blind to (and hence not limited by) the global event structure.

our approach is agnostic to the actual parsing models used, so we can easily incorporate models that can parse DAGs (Sagae and Tsujii, 2008).


If people only have access to a small amount of data, they may get a biased point of view.

investigating large amounts of data is a timeconsuming job.


We regard each of noun words as a candidate for SE/OE, and each of adjective (or verb) words as a candidate for PR.

this candidate detection has serious problems as follows: 4) There are many actual SEs, OEs, and PRs that consist of multiple words.


As mentioned in the introduction section, we easily construct a linguistic-based keyword set, К ling .

we observe that К ling is not enough to capture all the actual comparison expressions.


Each word in the sequence is replaced with its POS tag in order to reflect various expressions.

as CKs play the most important role, they are represented as a combination of their lexicalization and POS tag, e.g., "같/pa 1 ."


After defining the seven comparative types, we simply match each sentences to a particular type based on the CK types; e.g., a sentence which contains the word "가장 ([ga-jang]: most)" is matched to "Superlative" type.

a method that uses just the CK information has a serious problem.


Active Learning (AL) is typically initialized with a small seed of examples selected randomly.

when the distribution of classes in the data is skewed, some classes may be missed, resulting in a slow learning progress.


Fortunately, our model has only a limited set of possible visible values, which allows us to use a better approximation by taking the derivative of equation 5: 1 In cases where computing the partition function is still not feasible (for instance, because of a large vocabulary), sampling methods could be used.


The written form of Arabic, Modern Standard Arabic (MSA), differs quite a bit from the spoken dialects of Arabic, which are the true "native" languages of Arabic speakers used in daily life.

due to MSA's prevalence in written form, almost all Arabic datasets have predominantly MSA content.


Possible sources of dialectal text include weblogs, forums, and chat transcripts.

weblogs usually contain relatively little data, and a writer might use dialect in their writing only occasionaly, forums usually have content that is of little interest or relevance to actual applications, and chat transcripts are difficult to obtain and extract.


The popular microblogging service Twitter (twitter.com) is one particularly fruitful source of user-created content, and a flurry of recent research has aimed to understand and exploit these data (Ritter et al., 2010;Sharifi et al., 2010;Barbosa and Feng, 2010;Asur and Huberman, 2010;O'Connor et al., 2010a;Thelwall et al., 2011).

the bulk of this work eschews the standard pipeline of tools which might enable a richer linguistic analysis; such tools are typically trained on newstext and have been shown to perform poorly on Twitter (Finin et al., 2010).


These methods usually learn a single model given a training set.

single models cannot deal with words from multiple language origins.


This method requires training sets of transliterated word pairs with language origin.

it is difficult to obtain such tagged data, especially for proper nouns, a rich source of transliterated words.


The process of authorship attribution consists of selecting markers (features that provide an indication of the author), and classifying a text by assigning it to an author using some appropriate machine learning technique.

to the general authorship attribution problem, the specific problem of attributing translated texts to their original author has received little attention.


Ideally, the footprint of the author is (more or less) unaffected by the process of translation, for example if the languages are very similar or the marker is not based solely on lexical or syntactic features.

to purely lexical or syntactic features, the semantic content is expected to be, roughly, the same in translations and originals.


For translated texts, a mix of authors and translators across authors is needed to ensure that the attribution methods do not attribute to the translator instead of the author.

there does not appear to be a large corpus of texts publicly available that satisfy this demand.


For the Federalist Papers the traditional authorship attribution markers all lie in the 95+ range in accuracy as expected.

the frame-based markers achieved statistically significant results, and can hence be used for authorship attribution on untranslated documents (but performs worse than the baseline).


Of these, 7 were also in DEviaNT's most-sure set.

dEviaNT was also able to identify TWSSs that deal with noun euphemisms (e.g., "don't you think these buns are a little too big for this meat?


unigram SVM w/o MetaCost is most sure about 130 sentences, 77 of which are true positives.


We address this chicken-and-egg problem with a data-driven NLU approach that segments and identifies multiple dialogue acts in single utterances, even when only short (single dialogue act) utterances are available for training.

to previous approaches that assume the existence of enough training data for learning to segment utterances, e.g.


That is, interlocutors who entrain achieve better communication.

the question of how best to measure this phenomenon has not been well established.


In a study of the Columbia Games Corpus, Gravano and Hirschberg (2009;2011) identify five speech phenomena that are significantly correlated with speech followed by backchannels.

they also note that individual speakers produced different combinations of these cues and varied the way cues were expressed.


The likelihood that a segment of speech will be followed by a backchannel increases quadratically with the number of cues present in the speech.

they note that individual speakers may display different combinations of cues.


Both prosodic features (extracted from the acoustic signal) and lexical features (extracted from the word sequence) have been shown to be useful for these tasks (Shriberg et al., 1998;Kim and Woodland, 2003;Ang et al., 2005).

access to labeled speech training data is generally required in order to use prosodic features.


The values k = 20 and r = 6 were selected on the dev set.

with bootstrapping, SCL (Blitzer et al., 2006) uses the unlabeled target data to learn domainindependent features.


For instance, as shown next, the fraction of query traffic containing "how to" has in fact been going up since 2007.

such anecdotal evidence cannot fully support claims about general behavior in query formulation.


Given the lack of syntactic parsers that are appropriate for search queries, we address this question using a more robust measure: the probability mass of function words.

to content words (open class words), function words (closed class words) have little lexical meaning -they mainly provide grammatical information and are defined by their syntactic behavior.


The motivation of most recent formalisms is to develop a constraint-based framework where you can incrementally add constraints to filter out unwanted scopings.

almost all of these formalisms are based on hard constraints, which have to be satisfied in every reading of the sentence.


On the one hand, although highly reliable, in addition to being expensive and time consuming, human evaluation suffers from inconsistency problems due to inter-and intraannotator agreement issues.

while being consistent, fast and cheap, automatic evaluation has the major disadvantage of requiring reference translations.


As seen from the table, Meteor is the automatic metric exhibiting the largest ranking prediction capability, followed by BLEU and NIST, while our proposed AM-FM metric exhibits the lowest ranking prediction capability.

it still performs well above random chance predictions, which, for the given average of 4 items per ranking, is about 25% for best and worst ranking predictions, and about 8.33% for both.


Word is usually adopted as the smallest unit in most tasks of Chinese language processing.

for automatic evaluation of the quality of Chinese translation output when translating from other languages, either a word-level approach or a character-level approach is possible.


White space serves as the word delimiter in Latin alphabet-based languages.

in written Chinese text, there is no word delimiter.


If a word-level metric is used, the word "伞" in the system translation will not match the word "雨伞" in the reference translation.

if the system and reference translation are segmented into characters, the word "伞" in the system translation shares the same character "伞" with the word " 雨 伞 " in the reference.


Unfortunately, common practice in reporting machine translation results is to run the optimizer once per system configuration and to draw conclusions about the experimental manipulation from this single sample.

it could be that a particular sample is on the "low" side of the distribution over optimizer outcomes (i.e., it results in relatively poorer scores on the test set) or on the "high" side.


However, alignment inference in neither of these works is exactly Bayesian since the alignments are updated by running GIZA++ (Xu et al., 2008) or by local maximization (Nguyen et al., 2010).

chung and Gildea (2009) apply a sparse Dirichlet prior on the multinomial parameters to prevent overfitting.


Most of these occurrences are in subject settings over articles that aren't required to modify a noun, such as that, some, this, and all.

in the BLLIP n-gram data, this rule is used over the definite article the 465 times -the second-most common use.


They described a direct evidence method, a transitivity method, and a clustering method for ordering these different kinds of modifiers, with the transitivity technique returning the highest accuracy of 90.67% on a medical text.

when testing across domains, their accuracy dropped to 56%, not much higher than random guessing.


(2010) used a Multiple Sequence Alignment (MSA) approach to order modifiers, achieving the highest accuracy to date across different domains.

to earlier work, both systems order full modifier strings.


The dialogue acts Yes and Agreement can be generated using canned text, such as "That is true" and "I agree with you".

complQ (complex Question), FactQ (Factoid Question), FactA (Factiod Answer) and YNQ (Yes/No Question) all require syntactic manipulation.


To acquire confident samples, we need to first decide how to evaluate the confidence for each event.

as an event contains one trigger and an arbitrary number of roles, a confident event might contain unconfident arguments.


This enables the extraction of interesting and unanticipated relations from text.

these patterns are often too broad, resulting in the extraction of tuples that do not represent relations at all.


Many intra-category relations represent listings commonly identified by conjunctions.

these patterns are identified by multiple intra-category relations and are excluded.


Previous work has shown that selecting the sentences containing the entities targeted by a given relation is enough accurate (Banko et al., 2007;Mintz et al., 2009) to provide reliable training data.

only (Hoffmann et al., 2010) used DS to define extractors that are supposed to detect all the relation instances from a given input text.


(Zelenko et al., 2002;Culotta and Sorensen, 2004;Culotta and Sorensen, 2004;Bunescu and Mooney, 2005;Zhang et al., 2005;Bunescu, 2007;Nguyen et al., 2009;Zhang et al., 2006).

such approaches can be applied to few relation types thus distant supervised learning (Mintz et al., 2009) was introduced to tackle such problem.


We use the YAGO version of 2008-w40-2 with a manually confirmed accuracy of 95% for 99 relations.


our multi-classifier has two orders of magnitude less of categories).

the only experiment that can give a realistic measurement is the one on hand-labeled test set (testing on data automatically labelled by DS does not provide a realistic outcome).


Typically, systems first identify negation/speculation cues and subsequently try to identify their associated cue scope.

the two tasks are interrelated and both require syntactic understanding.


(Kilicoglu and Bergler, 2008), (Rei and Briscoe, 2010), (Velldal et al., 2010), (Kilicoglu and Bergler, 2010), (Zhou et al., 2010).

manually creating a comprehensive set of such lexico-syntactic scope rules is a laborious and time-consuming process.


In all cases, the approaches used surface (word) patterns without coreference.

we use the structural features of predicate-argument structure and employ coreference.


(2009), while demonstrating high precision, do not measure recall.


As a result, the recall test set is biased away from "true" recall, because it places a higher weight on the "long tail" of instances.

this gives a more accurate indication of the system's ability to find novel instances of a relation.


In this case, interpolating the phrase tables no longer show improvements.

using the generated corpus alone achieves  -1.80 on average TER.


Filtering improves the quality of the transferred annotations.

when training a parser on the annotations we see that filtering only results in better recall scores for predicate labelling.


On the one hand words do change their meaning, after all this is what the present study is all about.

we assume that the meanings in a certain context window are stable enough to infer reliable results provided it is possible that the forms of the same words in different periods can be linked.


Our system performed well in the i2b2/VA Challenge, achieving a micro-averaged F 1 -measure of 93.01%.

two of the assertion categories (present and absent) accounted for nearly 90% of the instances in the data set, while the other four classes were relatively infrequent.


With more parameters (9.7k vs. 3.7k), which allow for a better modeling of the data, L 0 normalization helps by zeroing out infrequent ones.

the difference between our complex model and the best HMM (EM with smoothing and random restarts, 55%) is not significant.


As an example of a merge that failed, we tried merging Argument Types and Mutual Exclusivity, with the idea that if a system knows about the selectional preferences of different relationships, it should be able to deduce which relationships or types are mutually exclusive.

the κ score for this combined category was 0.410, significantly below the κ of 0.640 for Mutual Exclusivity on its own.


The construction of an annotated corpus involves a lot of work performed by large groups.

despite the fact that a lot of human post-editing and automatic quality assurance is done, errors can not be avoided completely [5].


We will compare our outcomes with the results that can be found with the approach of "variation detection" proposed by Meurers et al.

for space reasons, we will not be able to elaborately present this method and advise to read the referred work, we think that we should at least briefly explain its idea.


Therefore we suggest that the recall of our method is close to the value of 0.459.

of course we do not know whether the randomly introduced errors in our experiment are similar to those which occur in real treebanks.


We have only analysed the errors in the headmodifier annotation of the dependency relations in the English dependency treebank.

the same methodology can easily be applied to detect irregularities in any kind of annotations, e.g.


Determining relationship between any two points in the same chain can be done in constant time simply by comparing the pseudo-times, rather than following the in-chain links.

relationship between points in different chains can be found with a search in cross-chain links, which is dependent on the number of edges (i.e.


Much work has been done on Arabic computational morphology (Al-Sughaiyer and Al-Kharashi, 2004;Soudi et al., 2007;Habash, 2010).

the bulk of this work does not address formfunction discrepancy or morpho-syntactic agreement issues.


Smrž (2007b)'s work contrasting illusory (form) features and functional features inspired our distinction of morphological form and function.

unlike him, we do not distinguish between sub-functional (logical and formal) features.


NU-LEX's first trial demonstrated that it was suitable for general purpose parsing.


Many concept-colour associations, such as swan with white and vegetables with green, involve physical entities.

even abstract notions and emotions may have colour associations (honesty-white, danger-red, joy-yellow, anger-red).


Experiments with colour categories have been used both to show that language has an effect on thought (Brown and Lenneberg, 1954;Ratner, 1989) and that it does not (Bornstein, 1985).

that line of work does not explicitly deal with word-colour associations.


It is natural for physical entities of a certain colour to be associated with that colour.

abstract concepts such as danger and excitability are also associated with colours-red and orange, respectively.


As pointed out in Section 2, there is prior work on emotions evoked by colours.

here we investigate the colours associated with emotion words.


Recent studies have shown that inversion transduction grammars are reasonable constraints for word alignment, and that the constrained space could be efficiently searched using synchronous parsing algorithms.

spurious ambiguity may occur in synchronous parsing and cause problems in both search efficiency and accuracy.


In itself, this should not cause a dramatic difference in performance, as the two systems perform similarly (Hoang and Koehn, 2008).

there are a number of other differences between the two systems.


Word dictionary probabilities can be directly estimated by IBM1 models.


This system obtains the worst results of all.

( ) is the most similar model to the best system in (Alabau et al., 2010).


Log-linear models show a bit of improvement with respect to IBM models.

linear interpolated models perform the best.


When applying it on a MT system hypothesis and a reference translation, it computes how much effort would be needed to obtain the reference from the hypothesis, possibly independently of the appropriateness of the alignments produced.

if we consider instead a pair of sentential paraphrases, it can be used to reveal what subsentential units can be aligned.


In theory, different strategies should produce equivalent translation results.

because decoding always involves pruning, we show that different strategies do have a significant effect in translation quality.


By mining a dictionary and naively incorporating it into a translation system, one can only do slightly better than baseline.

with a more clever integration, we can close about half of the gap between baseline (unadapted) performance and an oracle experiment.


The most obvious way of eliminating problematic unary rules would be converting grammars into Chomsky normal form.


We rely on GHKM rules for reordering when we use the monotonic glue rules.

we can also allow glue rules to reorder constituents.


This makes available a host of preexisting adaptation algorithms for improving over supervised results.

we argue that it may be 8 The feature normalization in Step 1 is important to ensure that the weight magnitudes are comparable.


These include methods where source sentences are divided into syntactic chunks or clauses and the translations are merged later (Koehn and Knight, 2003;, methods where syntactic constraints or penalties for reordering are added to a decoder (Yamamoto et al., 2008;Cherry, 2008;Marton and Resnik, 2008;Xiong et al., 2010), and methods where source sentences are reordered into a similar word order as the target language in advance (Katz-Brown and Collins, 2008;Isozaki et al., 2010).

these methods did not use document-level context to constrain reorderings.


In this case, a reordering constraint to translate " " as a single block can reduce incorrect reorderings and improve the translation quality.

it is difficult to predict what should be translated as a single block.


propose an online learning algorithm with soft margins to handle noise in training data.

the work does not consider the confidence associated with estimated feature weights.


However, the work does not consider the confidence associated with estimated feature weights.

the CW online algorithm in the later does not consider the case where the training data is noisy.


Traditionally, the cosine similarity measure is used to evaluate the likeness of two term-frequency representations.


It has been shown that better alignment quality generally leads to better results (Ganchev et al., 2008).

the relationship between the word alignment quality and the results is not straightforward, and it was shown in (Vilar et al., 2006) that better alignments in terms of F-measure do not always lead to better translation quality.


The method based on scores showed a good performance for the Chinese-English language pair, but the performance for the English-French pair was similar to the MSD model.

the method based on context improves the results on both pairs.


English used to have a T/V distinction until the 18th century, using you as V and thou as T pronoun.

in contemporary English, you has taken over both uses, and the T/V distinction is not marked morphosyntactically any more.


This allows us to train and evaluate a monolingual English classifier for this phenomenon.

two problems arise on the way: German has three relevant personal pronouns: du, sie, and ihr.


On the German side, we assign the T/V labels to pronouns, and the most straightforward way of setting up annotation projection would be to label their word-aligned English pronouns as T/V.

pronouns are not necessarily translated into pronouns; additionally, we found word alignment accuracy for pronouns, as a function of word class, to be far from perfect.


We follow here the same general idea and aim, in a first step, at improving the comparability of a given corpus while preserving most of its vocabulary.

unlike the previous work, we show here that it is possible to guarantee a certain degree of homogeneity for the improved corpus, and that this homogeneity translates into a significant improvement of both the quality of the resulting corpora and the bilingual lexicons extracted.


Since the content relatedness in the comparable corpus is basically reflected by the relations between all the possible bilingual document pairs, we use here the number of document pairs to represent the scale of the comparable corpus.

the weight β can thus be defined as the proportion of possible document pairs in the current comparable corpus (C e 1 , C f 2 ) to all the possible document pairs, which is: where # d (C) stands for the number of documents in C. this measure does not integrate the relative length of the French and English parts, which actually impacts the performance of bilingual lexicon extraction.


6. Letters 'q', 'k' and 'c' are often mixed up with each other because they sound alike in English although they are apart on the keyboard.

the three letters are not connected in Fig.


Prior studies of peer review in the Natural Language Processing field have not focused on helpfulness prediction, but instead have been concerned with issues such as highlighting key sentences in papers (Sandor and Vorndran, 2009), detecting important feedback features in reviews (Cho, 2008;, and adapting peer-review assignment (Garcia, 2010).

given some similarity between peer reviews and other review types, we hypothesize that techniques used to predict review helpfulness in other domains can also be applied to peer reviews.


Note that in isolation, MET (paper ratings) are not significantly correlated with peer-review helpfulness, which is different from prior findings of product reviews (Kim et al., 2006) where product scores are significantly correlated with product-review helpfulness.

when combined with other features, MET does appear to add value (last row).


Consider a contentious instance in a small dataset where 7 out of 15 Turkers (a minority) classified it as Error.

it might easily have happened that 8 Turkers (a majority) classified it as Error instead of 7.


While this example uses the same dataset for evaluating two systems, the procedure is general enough to allow two systems to be compared on two different datasets by simply examining the two plots.

two potential issues arise in that case.


The first is that the bin sizes will likely vary across the two plots.

this should not be a significant problem as long as the bins are sufficiently large.


A system using "social information" to find friend groups may work well in the latter case, but might not effectively suggest correct group members in the former case.

a system using "textual information" may be effective in the first case, but is probably weak in finding friends in the second case.


The cluster of an OOV word w can be defined as the cluster whose centroid is closest to the feature vector of w. The formerly removed high-frequency words are added as singleton clusters to produce a complete clustering.

ooV words can only be assigned to the original k-means clusters.


The c 1 model with θ = 1 is specialized for predicting words after unknown nouns and cardinal numbers and two thirds of the unknown words are of exactly that type.

with rising θ, other word classes get a higher influence and different probability distributions are superimposed.


The above grammar could capture inner-syllable dependencies.

the selection of the target characters also depend on the context.


These support the assumption that the context information are helpful to identify syllable equivalents.

the collocation grammars do not further improve performance.


Several results in the word segmentation literature suggest that description length provides a useful estimate of segmentation quality in fully unsupervised settings.

since the space of potential segmentations grows exponentially with the length of the corpus, no tractable algorithm follows directly from the Minimum Description Length (MDL) principle.


Of course, existing paraphrasing approaches do not explicitly account for redundancy, and hence this evaluation is not completely fair.

these findings suggest that redundancy may be an important issue to consider when developing and evaluating data-driven paraphrase approaches.


3, assumes that their entailment probability is independent of the rest of the hypothesis.

when the number of covered hypothesis terms increases the probability that the remaining terms are actually entailed by T increases too (even though we do not have supporting knowledge for their entailment).


The factor graph corresponding to this model is outlined in Figure 1b.

the fully supervised model might benefit from factors that directly connect the document variable, y d , with the inputs s. as argued by täckström and McDonald (2011), when only document-level supervision is available, the document variable, y d , should be independent of the input, s, conditioned on the latent variables, y s .


Esuli and Sebastiani (2006) used WordNet to determine polarities of words, which can include nouns.

dictionaries do not contain domain specific information.


It expresses a neutral feeling from the person.

it also implies a negative opinion about "hump," which indicates a product feature.


For example, for "voice quality", people can say "good voice quality" or "bad voice quality."

for features with context dependent opinions, people often have a fixed opinion, either positive or negative but not both.


Because the global hierarchical categorization can avoid the drawbacks about those high-level irrecoverable error, it is more popular in the machine learning domain.

the taxonomy is defined artificially and is usually very difficult to organize for large scale taxonomy.


There are several different theories about relative prominence assignment in noun-noun (henceforth, NN) compounds, such as the structural theory (Bloomfield, 1933;Marchand, 1969;Heinz, 2004), the analogical theory (Schmerling, 1971;Olsen, 2000), the semantic theory (Fudge, 1984;Liberman and Sproat, 1992) and the informativeness theory (Bolinger, 1972;Ladd, 1984).

1 in most studies, the different theories are examined and applied in isolation, thus making it difficult to compare them directly.


For n-gram models, Property 5 trivially holds since BO n−1 (w i−1 1 ) and Φ n (w i−1 1 ) are defined as sets of sequences ending with w i−1 i−n+2 and w i−1 i−n+1 with the former clearly being a superset of the latter.

when Φ can be arbitrary, e.g., a decision tree, that is not necessarily so.


Van den Bosch 2005proposes a decision-tree classifier which has been applied to training datasets with more than 100M words.

his model is non-probabilistic and thus a standard comparison with probabilistic models in terms of perplexity isn't possible.


The most commonly used composition function adds the probabilities of the words in a sentence together, and then divides by the number of words in that sentence.

to reduce redundancy, once a sentence has been chosen for summary inclusion, the probability distribution is recalculated such that any word that appears in the chosen sentence has its probability diminished.


One approach that could achieve this would be to build separate stopword lists for specific domains, and there are approaches to automatically build such lists (Lo et al., 2005).

a list-based approach cannot take context into account and therefore, among other things, will encounter problems with polysemy and synonymy.


Like (Haghighi and Vanderwende, 2009), (Daumé and Marcu, 2006), and (Barzilay and Lee, 2004), we model words as being generated from latent distributions.

instead of background, content, and document-specific distributions, we model all words in a document set as being there for one of only two purposes: a semantic (content) purpose, or a syntactic (functional) purpose.


It is natural to select the explicit comparative sentences as comparative summary, because they express comparison explicitly in good qualities.

they do not appear frequently in regular news articles so that the coverage is limited.


linguistic and psychological studies (cited above) show that such phenomena are indeed worth modelling in an NLG system.


the corpus structure to represent the content of the summaries.

the Regional portion of the dataset seems to contribute a significant amount of noise to the hierarchy, leading to a loss in performance for those models.


Thus, pruning constituents in lower cells directly affects the overall efficiency of parsing.

with the grammar loop method there is a constant number of grammar access operations (i.e., the number of grammar rules) and the number of active states in each child cell has no impact on efficiency.


According to the criteria used in Zhu and Zhu (2010), any CTB-style constituents with "认为" being the left boundary are thought to be inconsistent with the bracketing structure of the TCT-style parse and will be pruned.

if we prune such "inconsistent" constituents, the correct conversion result (right side of Fig.


Moreover, basic tie-breaking variants and lexical augmentation are insufficient to achieve competitive accuracies.

1 SDP is dramatically improved in both speed and accuracy when a simple, unlexicalized PCFG is used for coarseto-fine pruning (and tie-breaking).


Bansal and Klein (2010) use a carefully pa-rameterized weighting of the substructures in their grammar in an effort to extend the original DOP1 model (Bod, 1993;Goodman, 1996a).

for SDP, the grammar is even simpler (Goodman, 2003).


Their results hold up without pruning: the results of the unpruned version are only around 0.5% less (in parsing F1) than the results achieved with pruning (see Table 1).

in the case of our shortest-derivation parser, the coarse-pass is essential for high accuracies (and for speed and memory, as always).


These methods are effective because they tune the system to maximize an automatic evaluation metric such as BLEU, which serve as surrogate objective for translation quality.

we know that a single metric such as BLEU is not enough.


(2010) who applied multi-task learning for improved generalization in n-best reranking.


and tuning the full set of 180,000 features are not significant.

scaling all features to the full training set shows significant improvements for algorithm 3, and especially for algorithm 4, which gains 0.8 BLEU points over tuning 12 features on the development set.


Any remaining errors are typically corrected manually.

it may be more useful to give users more control during the input stage, instead of having a post-processing step for error correction.


The role of the haptic model and PLI model will be described in the following sub-sections.

similar to having an acoustic model as a statistical representation of the phoneme sequence generating the observed acoustic features, a haptic model is used to model the PLI sequence generating the observed haptic inputs, H. The haptic likelihood can be factorised as where  it is also possible to have a non-diagonal matrix for p(h i |l i ) in order to accommodate typing errors, so that non-zero probabilities are assigned to cases where For handwriting input, h i denote a sequence of 2dimensional feature vectors, which can be modelled using Hidden Markov Models (HMMs) (Rabiner, 1989).


If M = N , the PLI model likelihood, P (L|W), can be expressed in the following form: where P (l i |w i ) is the likelihood of the ith word, w i , generating the ith PLI, l i .

since each word is represented by a unique PLI (the initial letter) in this work, the PLI model score is given by if N = M , insertions and deletions have to be taken into consideration: where represents an empty token.


This is not surprising since key taps are much quicker to generate compared to handwriting gestures.

the individual speech and letter input speed are faster for asynchronous mode because users do not need to multi-task.


Therefore, in clean condition, the acoustic models are able to recover some of the errors introduced by the handwriting recognizer, bringing the LER down to as low as 0.3%.

in noisy conditions, the LER performance is similar to those using keyboard input.


The approach used in this paper is to build a standard FST for the current examination topic.

the annotation of the corpus is necessary before the building.


We used the Wordnet::Similarity software package (Pedersen et al., 2004) to calculate the similarity between every two words at first.

the performance's reduction of the AES system indicates that the similarity is not good enough to extend the FST model.


As a usual best feature for AES, the length shows its outstanding performance in CRR transcription.


In regular text essay scoring, the BOW algorithm can have excellent performance.

in certain situations, such as towards ASR transcription of oral English speech, its weakness of sequence neglect will be magnified, leading to drastic decline of performance.


Turning to the hybrid condition, the performance of Full features is surprisingly good, probably because we have more available training data than the other two conditions.

with contextual features removed, our features perform quite similarly to those of Hernault et al.


Topic segmentation approaches range from simple heuristic methods based on lexical similarity (Morris and Hirst, 1991;Hearst, 1997) to more intricate generative models and supervised methods (Georgescul et al., 2006;Purver et al., 2006;Gruber et al., 2007;Eisenstein and Barzilay, 2008), which have been shown to outperform the established heuristics.

previous computational work on conversational structure, particularly in topic discovery and topic segmentation, focuses primarily on content, ignoring the speakers.


For example: models having sticky topics over ngrams (Johnson, 2010), sticky HDP-HMM (Fox et al., 2008); models that are an amalgam of sequential models and topic models (Griffiths et al., 2005;Wal-lach, 2006;Gruber et al., 2007;Ahmed and Xing, 2008;Boyd-Graber and Blei, 2008;Du et al., 2010); or explicit models of time or other relevant features as a distinct latent variable (Wang and McCallum, 2006;Eisenstein et al., 2010).

sITs jointly models topic and individuals' tendency to control a conversation.


As shown in column Filled our approach returns less triples than other systems, explaining low recall.

our system achieves the highest precision for the complete task of temporally anchored relation extraction.


We note that this is not always the case: for example, the entailment graph in Figure 2 is not an FRG, because 'X annex Y' entails both 'Y be part of X' and 'X invade Y', while the latter two do not entail one another.

we hypothesize that this scenario is rather uncommon.


To conclude, TNF learns transitive entailment graphs of good quality much faster than Exactgraph.

our experiment utilized an available data set of moderate size; we expect TNF to scale to large data sets (that are currently unavailable), where other baselines would be impractical.


Similar to these methods, our algorithm capitalizes on surface linguistic cues to learn preconditions from text.

our only source of supervision is the feedback provided by the planning task which utilizes the predictions.


Like us, Ozbal and colleagues use both a textual model and a visual model (as well as Google adjective-noun cooccurrence counts) to find the typical color of an object.

their visual model works by analyzing pictures associated with an object, and determining the color of the object directly by image analysis.


It has also been suggested that this setting requires morphological generation because the bitext may not contain all inflected variants (Minkov et al., 2007;Toutanova et al., 2008;Fraser et al., 2012).

using lexical coverage experiments, we show that there is ample room for translation quality improvements through better selection of forms that already exist in the translation model.


The ATB does not contain animacy annotations, so our agreement model cannot discriminate between these two cases.

alkuhlani and Habash (2011) have recently started annotating the aTB for animacy, and our model could benefit as more data is released.


Our work uses the same training criterion and is based on the same generative story.

we use a new training procedure whose critical parts have constant time and memory complexity with respect to the vocabulary size so that our methods can scale to much larger vocabulary sizes while also being faster.


We use a greedy method similar to (Koehn and Knight, 2002) for extending a given lexicon, and we implicitly also use the frequency as a feature.

we perform fully unsupervised training and do not start with a seed lexicon or use linguistic features.


This defines a diagonal beam 4 when visualizing the lexicon entries in a matrix where both source and target words are sorted by their frequency rank.

note that the result of sorting by frequency   (e, f ) for which e is a translation candidate of f , while dots represent word pairs (e, f ) for which this is not the case.


In the formalism presented above, this means that each e i must be included in at most one span, and for each span u = v. Traditionally, these models are run in both directions and combined using heuristics to create many-to-many alignments (Koehn et al., 2003).

in order for one-to-many alignment methods to be effective, each f j must contain enough information to allow for effective alignment with its corresponding elements in e I 1 .


While this is often the case in word-based models, for characterbased models this assumption breaks down, as there is often no clear correspondence between characters.

in recent years, there have been advances in many-to-many alignment techniques that are able to align multi-element chunks on both sides of the translation (Marcu and Wong, 2002;DeNero et al., 2008;Blunsom et al., 2009;.


Discriminative models, which directly distinguish correct from incorrect hypothesis, are particularly attractive because they allow the inclusion of arbitrary features (Kuo et al., 2002;Roark et al., 2007;Collins et al., 2005); these models with syntactic information have obtained state of the art results.

both generative and discriminative LMs with long-span dependencies can be slow, for they often cannot work directly with lattices and require rescoring large N -best lists (Khudanpur and Wu, 2000;Collins et al., 2005;Kuo et al., 2009).


In other words, they search in the joint space of word sequences present in the lattice and their syntactic analyses; they are not guaranteed to produce a syntactic analysis for all hypotheses.

substructure sharing is a general purpose method that we have applied to two different algorithms.


The lattice parser therefore, is itself a language model.

our tools are completely separated from the ASR system, which allows the system to create whatever features are needed.


(2009) use a real-valued representation for vowels (formant values), but assume no variability in consonants, and treat each word token independently.

our model uses a symbolic representation for sounds, but models variability in all segment types and incorporates a bigram word-level language model.


Several other related systems work directly from the acoustic signal and many of these do use naturalistic corpora.

they do not learn at both the lexical and phonetic/acoustic level.


Firstly, we show that pregrouping multiword expressions before parsing with a state-of-the-art recognizer improves multiword recognition accuracy and unlabeled attachment score.

it has no statistically significant impact in terms of F-score as incorrect multiword expression recognition has important side effects on parsing.


It indicates that both discriminative strategies are of interest in locating multiword adjectives, determiners and prepositions; the pre-grouping method appears to be particularly relevant for multiword nouns and adverbs.

it performs very poorly in multiword verb recognition.